ti tl e: “Music Data Dashboard”
ou tp ut:
fl exdashboard::flex_dashboard:
theme: cosmo
version: 4
da te : “2025-03-14”
My C ompMus Portfolio - Exploring Mood in AI-Generated Tracks

Introduction

Background & Track Selection

This dashboard presents a comparative analysis of two AI-generated tracks I created using Stableaudio AI. One track explores ambient minimalism, while the other is a high-energy breakbeat.

Track 1: Meditative Ambient Soundscape
- Tags: Ambient, Post-Rock, Minimalist, Reverb, 60 BPM

Track 2: Energetic Breakbeat Rave
- Tags: Breakbeat, Acid Breaks, Chaotic, 135 BPM

These were selected to reflect contrasting moods and musical energies—one calm and introspective, the other intense and rhythmic.

As an Information Science student, both the world of computational music analysis and music production were fairly new to me at the start of this project. While I was familiar with working with data, exploring music through its features—like tempo, valence, or timbre—presented were entirely novel.

Starting off, I experimented with multiple AI music generators, but many of them felt too rigid or unpredictable. Eventually, I settled on Stableaudio AI for its detailed prompt control and more satisfying sonic results. My aim was to create two contrasting tracks: one calm and ambient, the other chaotic and energetic, so that I could explore the musical and data-level differences more clearly across various analytical features.

Had I had more time or production experience, I would have loved to compose or produce my own music from scratch. However, given the time constraints and learning curve, using AI allowed me to focus more on the analytical side of this project—treating the AI tracks as case studies to compare both with each other and with the class corpus.

# AI -Generated Tracks
Fo r this project, I generated two original pieces of music using Stableaudio AI (Stableaudio). My goal was to create two strongly contrasting tracks—one meditative and slow-paced, the other aggressive and rhythm-heavy. This contrast allowed for more insightful comparisons when analyzing musical features across mood, energy, and structure.
To s hape each track, I wrote detailed prompts inspired by genre tags from RateYourMusic.com, including emotional and instrumental descriptors.
** Tr ack 1: Meditative Ambient Soundscape**
** St Le Go yle:** Ambient, Post-Rock, Cinematic ngth:** 2 minutes al:** A calm, meditative ambient with minimal instrumentation.
** Am Ta bi gs Used: ent, Post-Rock, Cinematic, Ethereal, Soothing, Meditative, Minimalist, Warm Subtle Bass, Deep Drones, Airy Pads, Textures, Analog Synths, Field Recordings, Wind Sounds, Reverb, 60 BPM**

Track 2: Energetic Breakbeat Rave

Style: Breakbeat, Acid Breaks, 90s Rave
Length: 2 minutes
Goal: A high-energy, chaotic breakbeat track.

Tags Used:
Breakbeat, Acid Breaks, 90s Rave, Energetic, Raw, Funky, Chaotic, Breakbeats, Deep Bass, Distorted 808, Acid Bass, Filtered Chords, Reversed Pads, Vocal Chops, 135 BPM

Track Creation

Creating the tracks involved three steps:

  1. Prompt Design:
    I began by exploring genre tags on RateYourMusic, collecting descriptors that captured the mood, instrumentation, and texture I wanted. I made sure to include detailed keywords around tempo, style, and sound design to give Stableaudio clear guidance.

  2. Generation:
    I then entered these prompts into Stableaudio AI to generate multiple versions of each track. It took some trial and error to get the sonic qualities I envisioned, especially for the ambient piece, which some required subtlety in dynamics and space.

  3. Finalization:
    Once I had promising outputs, I evaluated them critically—listening for coherence, emotional impact, and production quality. I selected the strongest versions, exported them as MP3s, and prepared them for analysis in this dashboard.

With both tracks finalized, the next step was to dive into the data, starting with an exploration of its features.

Explorative Visualization - Scatterplot

To begin my analysis, I wanted a straightforward visualisation that would let me place my own tracks within the context of the class corpus. I chose a scatterplot because it clearly separates each data point, which helped me to quickly identify how my tracks compare in terms of danceability and tempo, as these two features often shape the overall feel of a track.

Below is a scatterplot of the Danceability compared to the Tempo of the tracks. My track 1 (ambient) is marked red, track 2 (breakbeat) is blue.

## Warning in geom_point(data = compmus_data %>% filter(filename == "evan-l-1"), :
## Ignoring unknown aesthetics: text
## Warning in geom_point(data = compmus_data %>% filter(filename == "evan-l-2"), :
## Ignoring unknown aesthetics: text

There appears to be no set correlation between the danceability and tempo of the tracks. However, an interesting pattern emerges: there are two clusters—one with low danceability, and another with high danceability, while the tempo does not differ much.

Regarding my own tracks:

One particularly surprising observation is how the AI interpreted the second song’s tempo. While I set it to 135 BPM, it was classified as 93 BPM. This suggests that the AI might have emphasized a different rhythmic structure or half-time feel in its classification.

Overall, the plot allowed me to see individual track positions and emerging clusters. As visible here, there’s a group of tracks with low danceability and another with high danceability, while tempo remains relatively consistent across both clusters.This helped me gain more perspective into where my own tracks ‘live’ compared to the others, which created a solid mental foundation for further explorations.

Chromagram

Chromagrams for own tracks

Before diving into the analysis, it’s helpful to briefly explain what a chromagram is. A chromagram is a visual representation of harmonic content in music. It maps audio to the 12 pitch classes (C, C#, D, …, B), showing how energy is distributed across pitches over time—regardless of octave. This is especially useful when analyzing chord progressions, tonality, or harmonic density.

Technical Issues

Creating these chromagrams proved to be a bit of a journey for me. I initially struggled because I missed the session where we were introduced to the compmus.R helper file. As I did not know it existed, I tried calculating chromagrams (and later, self-similarity matrices) using external packages like tuneR and seewave.

Eventually, I did manage to get my plots working, though it took a lot of trial and error. Even after discovering the compmus.R file, I ran into some persistent bugs—possibly due to updates to the helper functions or conflicts in how I had structured my analysis.For now, I’ve kept my original chromagram approach intact, since it renders reliably in R.

One thing I did notice was how the resulting spectrograms could benefit from better log-frequency mapping. Without it, the lower frequencies—especially in my ambient track—appear faint or almost non-existent. This is a fair reflection though, as the track is meant to be minimal and quiet.

## This took quite a lot of time to display this graphic, you may set 'fastdisp=TRUE' for a faster, but less accurate, display

Chromagram Results

Track 1 — Evan-l-1.wav (Ambient):

Energy is concentrated in the lower frequency range (below ~5 kHz)

The overall amplitude is low, reflecting the sparse, ambient atmosphere

The structure is fluid and minimal, with little rhythmic emphasis

Track 2 — Evan-l-2.wav (Breakbeat)

Frequency content spans a much wider range, reaching above 10 kHz

Brighter colors indicate greater amplitude and more harmonic variation

Suggests a more rhythmic, melodic, and dynamic structure, consistent with breakbeat styles

So, although the chromagram for the ambient track appears faint and minimal, this is true to the source material which was designed to be slow, textured, and atmospheric. In contrast, the breakbeat track is vibrant and information-dense, with sharper contrasts and more visible harmonic shifts.

Overall, the chromagrams helped me visually confirm the core differences I aimed for between these two pieces, which is the perceived strength of sound, as one track is quiet and soft while the other is loud and intense.

Self Similarity Matrices

After exploring harmonic content through chromagrams, I turned to self-similarity matrices (SSMs) to better understand the internal structure of my two tracks.

SSMs compare every moment in a song with every other moment, based on chosen features like chroma or timbre. This creates a square matrix where repeated or similar sections appear as distinct patterns—such as diagonal lines (repetitions), blocks (sections), or checkerboards (alternating segments). They’re very useful for spotting structural repetition, dynamic contrast, and transitions that might not be immediately audible.

Within the context of my musical exploration these SSMs helped me explore how much variety or repetition exists in my tracks—and whether those patterns aligned with what I intended in the composition prompts. As with the chromagrams, I used external packages to generate my self-similarity matrices (tuneR, seewave, and melfcc() from phonTools). Luckily, the output remains clear and useful, even if slightly stylistically different from the class standard.

## Warning in melfcc(wave, numcep = 12): Processing for more than one channel not
## yet implemented, using the first channel only ...
## Warning in melfcc(wave, numcep = 20): Processing for more than one channel not
## yet implemented, using the first channel only ...
## Warning in melfcc(wave, numcep = 12): Processing for more than one channel not
## yet implemented, using the first channel only ...
## Warning in melfcc(wave, numcep = 20): Processing for more than one channel not
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SSM Interpretation

  • Evan-l-1.wav - Ambient
    • The chroma SSM is fairly uniform, with only mild internal variation. This fits the track’s ambient purpose: minimal harmonic shifts and slow movement.
    • The timbre SSM is smooth and almost static, reflecting the consistent use of textures and sparse instrumentation.
  • Evan-l-2.wav - Breakbeat
    • The chroma SSM features a checkerboard-like structure, which suggests the presence alternating harmonic material or call-and-response phrasing.This is apparently typical of fast-paced breakbeat.
    • The timbre SSM shows greater diversity and shifting textures, likely due to drum fills, chopped vocal samples, and synthesized layers that I requested in my prompt.

Despite being AI-generated, both tracks exhibit recognizable structural patterns that align with their intended genres. The ambient track relies on repetition and slow shifts, while the breakbeat track emphasizes variety and rhythmic segmentation. These SSMs were one of the clearest visual confirmations of that difference.

Chordo/Keygram - Visualizing the keys

Chordograms offer a great way to visualize how harmonic templates such as keys align with the audio over time. They are derived from chroma features, but rather than showing raw pitch-class energy, they show how closely the signal matches known musical templates like major/minor chords.

For this step, I finally managed to use the class’s compmus.R functions after struggling initially with the wrong feature format. Now that I had access to my .json chroma features, I could apply the pitch matching function. I wanted to generate keygrams to better understand the tonal content of each track. Since both songs belong to abstract or electronic genres—ambient and breakbeat—it wasn’t obvious what key either was in just by listening. So, a visualisation of harmonic similarity across key templates offered an analytical alternative.

I used the ‘viridis’ colour scale, in which darker colours imply a stronger presence, while lighter colours indicate absence.

## Keygram Interpretation

Conclusively, the ambient track displays strong harmonic minimalism, while the breakbeat track displays harmonic variability. It is interesting how both of these reflect their respective genre conventions, and how it would be easy to tell which track is which from the keygrams alone.

Tempograms - Analysis of tempo structure

Histogram for tempo class corpus

Before analyzing my own tracks’ tempo structures in detail, I first wanted to understand how tempo is distributed across the entire class corpus. My goal was to see where my own AI-generated tracks might fit within the broader spectrum of musical pacing in the dataset.

To do this I chose to visualize the tempo of each track using a histogram. A bar plot is one of the most straightforward ways to represent frequency distributions, and in this context, it clearly displays which tempos are most common in the class corpus. I also used Plotly to make the plot interactive, so individual tempo bins can be hovered over to reveal exact values—adding both clarity and precision to the visualization.

From the histogram, we can see that the most frequent tempo (mode) in the class corpus is 85 BPM, with a count of 16 tracks. This relatively slow pace is commonly associated with genres like hip-hop, downtempo electronic, or ballads—genres that often center around groove or mood rather than rapid movement. It also aligns roughly with the human resting heart rate, which may contribute to its perceived ‘naturalness’.

The tempo distribution also shows smaller peaks in higher ranges, this could hint at clusters of tracks with faster or more danceable tempos like those found in house, pop, or techno.

Tempograms for own songs

After establishing the previous benchmark, I visualized the tempograms of my two AI-generated songs. These tempograms are generated from the JSON feature files and show how rhythmic intensity evolves over the duration of each track.

## `summarise()` has grouped output by 'time'. You can override using the
## `.groups` argument.

## `summarise()` has grouped output by 'time'. You can override using the
## `.groups` argument.

  • Evan-l-1.wav - Ambient
    • The tempogram is fairly uniform and flat for the first half. This is consistent with how smooth and uniform the track is.
    • Around the 60-second mark, the regularity begins to break down, and we observe some fragmentation and drift in the tempo bands. This showcases the more layered elements that are audible in this track, such as the addition of audio tape samples.
  • Evan-l-2.wav - Breakbeat
    • This track displays clearly defined, horizontal tempo bands, which indicates a steady and repetitive beat.
    • The multiple stacked lines reflect regular subdivisions (ex: half-time, double-time) which is usual in breakbeat and rave music.
    • The activity is highly dynamic and colorful, which reflects the energetic and intense piece of music.
    • There’s a minor disturbance just past the halfway point—likely a bridge or breakdown before returning to the core rhythmic pattern.

Comparing these tempograms visually strengthens the contrast in structure between my two tracks. The ambient piece is loosely timed, smooth, and much more textural, while the breakbeat track is rigid, fast-paced, and driven by its beat. This rhythmic contrast further supports what was established in earlier keygrams and SSMs, as each track is clearly distinct in both feel and temporal organization.

# De ndrogram
To f inalize my feature exploration I wanted to visualize how different tracks group together based on their musical characteristics. Dendrograms, which visualize hierarchical clustering, are perfect for this kind of exploratory analysis: They show the relative similarity between tracks and form clusters where songs with similar musical features are grouped closer together.
## H ierarchical clustering on class corpus
I be gan by creating a dendrogram based on five track-level features: valence, arousal, danceability, tempo, and instrumentalness. This was mainly exploratory to give me a sense of how different the tracks in our class corpus really are when analyzed together.
<i Th mg is src=“index_files/figure-html/clustering_dendrogram-1.png” width=“672” /> general dendrogram already shows some distinct clustering patterns, although it’s based on a broader set of features.
## H ierarchical clustering on own tracks - Mood-based clustering
Si nc e my two AI-generated tracks are drastically different in energy and mood, I wanted to focus more directly on clustering the corpus based on the emotional and rhythmic character of each song. For this, I narrowed it down to just valence, arousal and tempo.
<i mg src=“index_files/figure-html/clustering_mood_only-1.png” width=“672” />
In t his mood-based dendrogram, tracks that cluster lower tend to be more similar in emotional character. In this case, that often means relaxed, calm, or lo-fi — including my ambient track. My breakbeat track, on the other hand, clusters higher up, which indicates that it’s more distinct and intense in mood and energy.
To c onclude, this dendrograms pattern aligns reasonably well with my own two tracks. My ambient piece is grouped low in the dendrogram, nestled among similarly relaxed tracks. Meanwhile, the breakbeat track is positioned much higher, reflecting its chaotic energy and rhythmic aggression. Seeing this contrast play out in the structure of the dendrogram was especially satisfying as it served as a visualization for something that is often only describable as a ‘vibe’ or ‘energy’, now being measurably present in the data.

Final Thoughts

Coming into this project without a strong background in either computational analysis or musicology, I wasn’t entirely sure what to expect. My experience with R was minimal and limited to a single data science course two years ago, and my musical understanding was more intuitive than technical. But working through this analysis gave me a surprisingly rich perspective on how musical qualities manifest in data.

What stood out to me the most was seeing the contrast between my two AI-generated tracks unfold across each visualization. Because the tracks were intentionally so different (one ambient and meditative, the other energetic and chaotic) the distinctions were clear, which made it much easier to understand what each method was actually showing.

Whether looking at chromagrams, self-similarity matrices, or keygrams, I could begin to interpret the visuals in a way that matched what I had heard intuitively. Even moments like sensing a key shift without knowing the actual notes, only to see that shift appear in the analysis, helped recontextualize how I listen to music altogether. It felt like learning a new language which allowed me to describe and understand music in ways I couldn’t before.

This kind of process could be especially valuable for beginners in musicological analysis, particularly those exploring electronic instrumental music. The contrast between tracks, when made visual, becomes a powerful learning tool. My own analysis could serve as a way to better understand these features for other ‘compmus beginners’ as well. Being able to see harmony, rhythm, structure, and energy not only supports deeper listening, but also helps bridge the gap between abstract sound and analytical clarity.